Correlation between aggregated molecular cancer subtypes and selected clinical features

Colon Adenocarcinoma (Primary solid tumor)

15 January 2014 | analyses__2014_01_15

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1DJ5D4Z

Overview

Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 12 different clustering approaches and 11 clinical features across 433 patients, 18 significant findings detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'PATHOLOGY.N.STAGE' and 'HISTOLOGICAL.TYPE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE'.
CNMF clustering analysis on RPPA data identified 4 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', and 'COMPLETENESS.OF.RESECTION'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'AGE', 'PATHOLOGY.M.STAGE', and 'COMPLETENESS.OF.RESECTION'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'AGE', 'PATHOLOGY.M.STAGE', and 'COMPLETENESS.OF.RESECTION'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'AGE', 'PATHOLOGY.M.STAGE', and 'COMPLETENESS.OF.RESECTION'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes do not correlate to any clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 11 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 18 significant findings detected.


Clinical Features	Time to Death	AGE	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION	COMPLETENESS OF RESECTION	NUMBER OF LYMPH NODES
Statistical Tests	logrank test	ANOVA	Chi-square test	Chi-square test	Chi-square test	Chi-square test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Chi-square test	ANOVA
mRNA CNMF subtypes	0.074 (1.00)	0.569 (1.00)	0.00763 (0.832)	0.739 (1.00)	0.0121 (1.00)	0.0507 (1.00)	0.0361 (1.00)	1.24e-05 (0.00149)		0.204 (1.00)	0.104 (1.00)
mRNA cHierClus subtypes	0.486 (1.00)	0.0114 (1.00)	0.268 (1.00)	0.468 (1.00)	0.741 (1.00)	0.699 (1.00)	0.19 (1.00)	1.87e-07 (2.39e-05)		0.454 (1.00)	0.988 (1.00)
Copy Number Ratio CNMF subtypes	0.961 (1.00)	0.334 (1.00)	0.0131 (1.00)	0.456 (1.00)	0.00108 (0.124)	0.00721 (0.793)	0.425 (1.00)	8.96e-10 (1.16e-07)	0.186 (1.00)	0.0964 (1.00)	0.228 (1.00)
METHLYATION CNMF	0.857 (1.00)	0.000507 (0.0589)	0.386 (1.00)	0.336 (1.00)	0.834 (1.00)	0.277 (1.00)	0.089 (1.00)	0.00846 (0.914)	0.481 (1.00)	0.279 (1.00)	0.793 (1.00)
RPPA CNMF subtypes	0.781 (1.00)	0.844 (1.00)	0.168 (1.00)	0.489 (1.00)	0.522 (1.00)	0.081 (1.00)	0.822 (1.00)	0.0364 (1.00)	0.5 (1.00)	0.00433 (0.48)	0.121 (1.00)
RPPA cHierClus subtypes	0.0516 (1.00)	0.379 (1.00)	0.316 (1.00)	0.355 (1.00)	0.18 (1.00)	0.175 (1.00)	0.261 (1.00)	0.268 (1.00)	0.32 (1.00)	0.217 (1.00)	0.0461 (1.00)
RNAseq CNMF subtypes	0.538 (1.00)	0.00413 (0.463)	0.0882 (1.00)	0.791 (1.00)	0.282 (1.00)	1.04e-06 (0.000131)	0.924 (1.00)	4.88e-06 (0.000605)	0.208 (1.00)	6.58e-05 (0.00783)	0.86 (1.00)
RNAseq cHierClus subtypes	0.523 (1.00)	0.00116 (0.132)	0.0885 (1.00)	0.648 (1.00)	0.35 (1.00)	2.44e-07 (3.1e-05)	0.352 (1.00)	0.0248 (1.00)	0.0545 (1.00)	1.1e-07 (1.42e-05)	0.426 (1.00)
MIRSEQ CNMF	0.0726 (1.00)	0.000285 (0.0333)	0.167 (1.00)	0.358 (1.00)	0.312 (1.00)	1.02e-05 (0.00124)	0.441 (1.00)	0.095 (1.00)	0.07 (1.00)	7.93e-06 (0.000967)	0.0625 (1.00)
MIRSEQ CHIERARCHICAL	0.122 (1.00)	0.0021 (0.237)	0.0107 (1.00)	0.659 (1.00)	0.0252 (1.00)	2.16e-06 (0.00027)	0.312 (1.00)	1 (1.00)	0.597 (1.00)	7.85e-06 (0.000965)	0.524 (1.00)
MIRseq Mature CNMF subtypes	0.395 (1.00)	0.12 (1.00)	0.466 (1.00)	0.205 (1.00)	0.0279 (1.00)	0.459 (1.00)	0.312 (1.00)	6.9e-05 (0.00815)	0.744 (1.00)	0.45 (1.00)	0.042 (1.00)
MIRseq Mature cHierClus subtypes	0.162 (1.00)	0.155 (1.00)	0.947 (1.00)	0.723 (1.00)	0.419 (1.00)	0.86 (1.00)	0.865 (1.00)	0.0703 (1.00)	0.334 (1.00)	0.445 (1.00)	0.069 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

Table S1. Description of clustering approach #1: 'mRNA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	38	61	30	24

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.074 (logrank test), Q value = 1

Table S2. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	75	13	0.9 - 52.0 (4.0)
subtype1	24	1	1.0 - 30.0 (1.5)
subtype2	27	8	1.0 - 41.0 (15.0)
subtype3	16	3	1.0 - 52.0 (8.0)
subtype4	8	1	0.9 - 1.0 (1.0)

Figure S1. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.569 (ANOVA), Q value = 1

Table S3. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	153	70.8 (11.5)
subtype1	38	72.4 (11.4)
subtype2	61	69.9 (11.7)
subtype3	30	72.0 (10.9)
subtype4	24	68.9 (12.3)

Figure S2. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'mRNA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00763 (Chi-square test), Q value = 0.83

Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	29	12	45	5	8	3	12	16	21	1
subtype1	6	1	14	3	3	1	3	1	5	0
subtype2	11	2	12	0	2	2	5	13	14	0
subtype3	7	6	9	2	1	0	3	0	1	1
subtype4	5	3	10	0	2	0	1	2	1	0

Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.739 (Chi-square test), Q value = 1

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	4	31	103	15
subtype1	1	7	24	6
subtype2	2	12	42	5
subtype3	0	7	19	4
subtype4	1	5	18	0

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0121 (Chi-square test), Q value = 1

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	94	28	31
subtype1	25	5	8
subtype2	27	15	19
subtype3	24	5	1
subtype4	18	3	3

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0507 (Chi-square test), Q value = 1

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A
ALL	129	21	1
subtype1	32	5	0
subtype2	47	14	0
subtype3	28	1	1
subtype4	22	1	0

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.0361 (Fisher's exact test), Q value = 1

Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	75	78
subtype1	25	13
subtype2	31	30
subtype3	10	20
subtype4	9	15

Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1.24e-05 (Fisher's exact test), Q value = 0.0015

Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	129	22
subtype1	25	13
subtype2	60	1
subtype3	22	7
subtype4	22	1

Figure S8. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'mRNA CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.104 (ANOVA), Q value = 1

Table S10. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	152	2.1 (4.5)
subtype1	38	2.7 (5.8)
subtype2	61	2.8 (4.8)
subtype3	29	0.7 (2.3)
subtype4	24	1.2 (2.6)

Figure S9. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S11. Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	57	46	50

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.486 (logrank test), Q value = 1

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	75	13	0.9 - 52.0 (4.0)
subtype1	33	9	0.9 - 52.0 (15.0)
subtype2	28	2	1.0 - 30.0 (1.0)
subtype3	14	2	0.9 - 34.0 (1.0)

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.0114 (ANOVA), Q value = 1

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	153	70.8 (11.5)
subtype1	57	71.8 (9.4)
subtype2	46	73.7 (10.9)
subtype3	50	67.0 (13.4)

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.268 (Chi-square test), Q value = 1

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE IIA	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA
ALL	29	12	45	5	8	3	12	16	21	1
subtype1	7	7	16	1	3	2	3	9	8	1
subtype2	9	3	14	4	3	1	5	1	5	0
subtype3	13	2	15	0	2	0	4	6	8	0

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.468 (Chi-square test), Q value = 1

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	4	31	103	15
subtype1	1	8	43	5
subtype2	1	10	28	7
subtype3	2	13	32	3

Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.741 (Chi-square test), Q value = 1

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	94	28	31
subtype1	33	13	11
subtype2	31	7	8
subtype3	30	8	12

Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.699 (Chi-square test), Q value = 1

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A
ALL	129	21	1
subtype1	48	8	1
subtype2	40	5	0
subtype3	41	8	0

Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.19 (Fisher's exact test), Q value = 1

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	75	78
subtype1	28	29
subtype2	27	19
subtype3	20	30

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1.87e-07 (Fisher's exact test), Q value = 2.4e-05

Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	129	22
subtype1	54	3
subtype2	27	18
subtype3	48	1

Figure S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'mRNA cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.988 (ANOVA), Q value = 1

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	152	2.1 (4.5)
subtype1	56	2.2 (4.9)
subtype2	46	2.2 (4.7)
subtype3	50	2.1 (3.9)

Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #3: 'Copy Number Ratio CNMF subtypes'

Table S21. Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	170	194	60

'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.961 (logrank test), Q value = 1

Table S22. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	340	63	0.1 - 140.4 (13.0)
subtype1	132	25	0.1 - 131.5 (14.3)
subtype2	160	29	0.1 - 140.4 (12.6)
subtype3	48	9	0.5 - 119.7 (11.5)

Figure S19. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.334 (ANOVA), Q value = 1

Table S23. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	423	67.2 (12.9)
subtype1	170	66.7 (12.1)
subtype2	193	68.1 (14.1)
subtype3	60	65.4 (11.5)

Figure S20. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0131 (Chi-square test), Q value = 1

Table S24. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	71	1	33	119	8	1	22	13	52	36	42	16	1
subtype1	21	0	6	41	2	1	8	8	24	17	26	11	0
subtype2	38	1	19	65	6	0	10	3	20	14	10	3	1
subtype3	12	0	8	13	0	0	4	2	8	5	6	2	0

Figure S21. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.456 (Chi-square test), Q value = 1

Table S25. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	11	75	290	47
subtype1	5	25	123	17
subtype2	6	36	126	25
subtype3	0	14	41	5

Figure S22. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.00108 (Chi-square test), Q value = 0.12

Table S26. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	248	101	74
subtype1	79	53	37
subtype2	133	33	28
subtype3	36	15	9

Figure S23. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.00721 (Chi-square test), Q value = 0.79

Table S27. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	318	50	7	1	41
subtype1	114	32	5	0	16
subtype2	157	11	1	1	21
subtype3	47	7	1	0	4

Figure S24. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.425 (Fisher's exact test), Q value = 1

Table S28. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	198	226
subtype1	76	94
subtype2	97	97
subtype3	25	35

Figure S25. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 8.96e-10 (Fisher's exact test), Q value = 1.2e-07

Table S29. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	367	55
subtype1	165	4
subtype2	147	46
subtype3	55	5

Figure S26. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.186 (Fisher's exact test), Q value = 1

Table S30. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	421
subtype1	2	168
subtype2	0	194
subtype3	1	59

Figure S27. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0964 (Chi-square test), Q value = 1

Table S31. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	297	3	24	24
subtype1	117	1	15	7
subtype2	140	2	4	14
subtype3	40	0	5	3

Figure S28. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.228 (ANOVA), Q value = 1

Table S32. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	400	2.0 (4.5)
subtype1	163	2.5 (5.1)
subtype2	181	1.8 (4.2)
subtype3	56	1.6 (3.3)

Figure S29. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #4: 'METHLYATION CNMF'

Table S33. Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	113	88	68

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.857 (logrank test), Q value = 1

Table S34. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	260	49	0.1 - 140.4 (13.3)
subtype1	107	20	0.1 - 140.4 (15.0)
subtype2	85	17	0.1 - 135.7 (13.8)
subtype3	68	12	0.1 - 102.4 (11.9)

Figure S30. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.000507 (ANOVA), Q value = 0.059

Table S35. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	268	65.1 (13.1)
subtype1	113	66.0 (12.7)
subtype2	88	67.9 (12.8)
subtype3	67	60.0 (12.9)

Figure S31. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.386 (Chi-square test), Q value = 1

Table S36. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	40	1	19	80	5	1	8	10	40	21	20	15	1
subtype1	15	0	7	31	1	1	4	5	16	8	11	10	0
subtype2	19	1	4	26	3	0	4	2	13	6	4	3	1
subtype3	6	0	8	23	1	0	0	3	11	7	5	2	0

Figure S32. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.336 (Chi-square test), Q value = 1

Table S37. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	7	41	187	33
subtype1	3	18	80	12
subtype2	3	18	56	10
subtype3	1	5	51	11

Figure S33. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.834 (Chi-square test), Q value = 1

Table S38. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	158	69	41
subtype1	63	31	18
subtype2	55	19	14
subtype3	40	19	9

Figure S34. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.277 (Chi-square test), Q value = 1

Table S39. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	188	28	6	1	41
subtype1	72	17	4	0	19
subtype2	64	5	1	1	14
subtype3	52	6	1	0	8

Figure S35. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.089 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	121	148
subtype1	46	67
subtype2	48	40
subtype3	27	41

Figure S36. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.00846 (Fisher's exact test), Q value = 0.91

Table S41. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	236	33
subtype1	107	6
subtype2	73	15
subtype3	56	12

Figure S37. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.481 (Fisher's exact test), Q value = 1

Table S42. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	266
subtype1	2	111
subtype2	0	88
subtype3	1	67

Figure S38. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.279 (Chi-square test), Q value = 1

Table S43. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	168	2	4	23
subtype1	71	0	3	8
subtype2	53	2	1	10
subtype3	44	0	0	5

Figure S39. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'METHLYATION CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.793 (ANOVA), Q value = 1

Table S44. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	246	1.9 (4.5)
subtype1	104	2.1 (5.7)
subtype2	80	1.7 (3.0)
subtype3	62	1.9 (3.6)

Figure S40. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S45. Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	80	128	27	96

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.781 (logrank test), Q value = 1

Table S46. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	275	50	0.1 - 140.4 (13.0)
subtype1	68	8	0.1 - 131.5 (10.2)
subtype2	100	21	0.1 - 140.4 (14.6)
subtype3	20	2	1.0 - 52.0 (15.4)
subtype4	87	19	0.1 - 135.7 (12.9)

Figure S41. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.844 (ANOVA), Q value = 1

Table S47. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	330	67.3 (13.0)
subtype1	80	66.8 (12.9)
subtype2	127	68.1 (12.3)
subtype3	27	66.9 (12.0)
subtype4	96	66.7 (14.3)

Figure S42. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.168 (Chi-square test), Q value = 1

Table S48. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	49	1	25	103	8	1	19	8	42	29	28	14	1
subtype1	10	0	9	20	3	0	2	3	14	10	5	4	0
subtype2	18	0	11	33	3	1	9	2	17	8	18	5	1
subtype3	3	1	1	12	0	0	2	0	3	3	1	1	0
subtype4	18	0	4	38	2	0	6	3	8	8	4	4	0

Figure S43. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.489 (Chi-square test), Q value = 1

Table S49. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	6	53	233	38
subtype1	0	12	59	9
subtype2	2	21	90	15
subtype3	0	3	22	1
subtype4	4	17	62	13

Figure S44. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.522 (Chi-square test), Q value = 1

Table S50. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	196	80	55
subtype1	42	23	15
subtype2	72	32	24
subtype3	17	6	4
subtype4	65	19	12

Figure S45. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.081 (Chi-square test), Q value = 1

Table S51. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	259	36	6	1	26
subtype1	65	6	3	0	5
subtype2	92	23	0	1	11
subtype3	24	2	0	0	1
subtype4	78	5	3	0	9

Figure S46. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.822 (Fisher's exact test), Q value = 1

Table S52. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	155	176
subtype1	38	42
subtype2	56	72
subtype3	13	14
subtype4	48	48

Figure S47. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0364 (Fisher's exact test), Q value = 1

Table S53. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	293	38
subtype1	68	12
subtype2	121	7
subtype3	23	4
subtype4	81	15

Figure S48. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'RPPA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.5 (Fisher's exact test), Q value = 1

Table S54. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	328
subtype1	2	78
subtype2	1	127
subtype3	0	27
subtype4	0	96

Figure S49. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.00433 (Chi-square test), Q value = 0.48

Table S55. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R2	RX
ALL	239	16	16
subtype1	60	1	6
subtype2	77	13	6
subtype3	26	1	0
subtype4	76	1	4

Figure S50. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'RPPA CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.121 (ANOVA), Q value = 1

Table S56. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	311	2.0 (4.5)
subtype1	74	2.9 (6.8)
subtype2	120	2.0 (3.8)
subtype3	27	1.7 (3.3)
subtype4	90	1.2 (2.6)

Figure S51. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S57. Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	29	57	64	181

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0516 (logrank test), Q value = 1

Table S58. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	275	50	0.1 - 140.4 (13.0)
subtype1	22	9	2.0 - 107.7 (13.9)
subtype2	53	9	0.1 - 135.7 (11.0)
subtype3	61	6	0.1 - 131.5 (12.1)
subtype4	139	26	0.1 - 140.4 (15.0)

Figure S52. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.379 (ANOVA), Q value = 1

Table S59. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	330	67.3 (13.0)
subtype1	29	69.4 (13.4)
subtype2	57	65.3 (13.5)
subtype3	64	66.1 (12.1)
subtype4	180	67.9 (13.1)

Figure S53. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.316 (Chi-square test), Q value = 1

Table S60. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	49	1	25	103	8	1	19	8	42	29	28	14	1
subtype1	2	0	3	13	1	0	2	0	4	1	2	0	0
subtype2	14	0	3	22	1	0	2	1	4	5	2	3	0
subtype3	8	0	2	22	1	0	0	3	9	9	4	6	0
subtype4	25	1	17	46	5	1	15	4	25	14	20	5	1

Figure S54. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.355 (Chi-square test), Q value = 1

Table S61. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	6	53	233	38
subtype1	0	2	24	3
subtype2	3	13	35	6
subtype3	1	8	47	8
subtype4	2	30	127	21

Figure S55. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.18 (Chi-square test), Q value = 1

Table S62. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	196	80	55
subtype1	19	6	4
subtype2	42	9	6
subtype3	34	15	15
subtype4	101	50	30

Figure S56. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.175 (Chi-square test), Q value = 1

Table S63. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	259	36	6	1	26
subtype1	23	2	0	0	2
subtype2	47	2	3	0	5
subtype3	49	7	3	0	4
subtype4	140	25	0	1	15

Figure S57. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.261 (Fisher's exact test), Q value = 1

Table S64. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	155	176
subtype1	17	12
subtype2	21	36
subtype3	30	34
subtype4	87	94

Figure S58. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.268 (Fisher's exact test), Q value = 1

Table S65. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	293	38
subtype1	26	3
subtype2	47	10
subtype3	55	9
subtype4	165	16

Figure S59. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'RPPA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.32 (Fisher's exact test), Q value = 1

Table S66. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	328
subtype1	0	29
subtype2	0	57
subtype3	2	62
subtype4	1	180

Figure S60. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.217 (Chi-square test), Q value = 1

Table S67. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R2	RX
ALL	239	16	16
subtype1	21	2	2
subtype2	46	0	2
subtype3	48	1	2
subtype4	124	13	10

Figure S61. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'RPPA cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0461 (ANOVA), Q value = 1

Table S68. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	311	2.0 (4.5)
subtype1	27	1.3 (2.5)
subtype2	54	1.1 (2.7)
subtype3	60	3.2 (7.4)
subtype4	170	1.9 (3.6)

Figure S62. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S69. Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	132	133	150

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.538 (logrank test), Q value = 1

Table S70. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	328	60	0.1 - 140.4 (13.0)
subtype1	121	22	0.1 - 140.4 (14.0)
subtype2	125	24	0.1 - 135.7 (12.5)
subtype3	82	14	0.9 - 92.8 (10.6)

Figure S63. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00413 (ANOVA), Q value = 0.46

Table S71. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	414	67.1 (13.1)
subtype1	132	65.4 (12.2)
subtype2	132	65.5 (14.3)
subtype3	150	69.9 (12.4)

Figure S64. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0882 (Chi-square test), Q value = 1

Table S72. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	66	1	31	120	9	1	22	13	50	35	42	15	1
subtype1	19	0	8	35	1	1	6	5	17	8	15	11	0
subtype2	21	1	11	44	2	0	4	3	19	13	9	3	1
subtype3	26	0	12	41	6	0	12	5	14	14	18	1	0

Figure S65. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.791 (Chi-square test), Q value = 1

Table S73. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	11	70	284	49
subtype1	3	22	90	17
subtype2	4	18	92	18
subtype3	4	30	102	14

Figure S66. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.282 (Chi-square test), Q value = 1

Table S74. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	242	99	73
subtype1	72	40	19
subtype2	81	27	25
subtype3	89	32	29

Figure S67. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 1.04e-06 (Chi-square test), Q value = 0.00013

Table S75. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	310	49	7	1	41
subtype1	83	20	6	0	22
subtype2	99	11	0	1	19
subtype3	128	18	1	0	0

Figure S68. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.924 (Fisher's exact test), Q value = 1

Table S76. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	198	217
subtype1	61	71
subtype2	64	69
subtype3	73	77

Figure S69. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 4.88e-06 (Fisher's exact test), Q value = 6e-04

Table S77. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	359	54
subtype1	128	4
subtype2	103	30
subtype3	128	20

Figure S70. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.208 (Fisher's exact test), Q value = 1

Table S78. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	412
subtype1	2	130
subtype2	1	132
subtype3	0	150

Figure S71. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 6.58e-05 (Chi-square test), Q value = 0.0078

Table S79. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	293	3	23	24
subtype1	82	0	5	10
subtype2	83	2	2	14
subtype3	128	1	16	0

Figure S72. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'RNAseq CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.86 (ANOVA), Q value = 1

Table S80. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	391	2.1 (4.5)
subtype1	123	2.2 (5.8)
subtype2	120	2.1 (4.1)
subtype3	148	1.9 (3.5)

Figure S73. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S81. Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	172	144	99

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.523 (logrank test), Q value = 1

Table S82. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	328	60	0.1 - 140.4 (13.0)
subtype1	94	15	0.9 - 83.8 (10.6)
subtype2	138	24	0.1 - 140.4 (13.1)
subtype3	96	21	0.1 - 135.7 (13.6)

Figure S74. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00116 (ANOVA), Q value = 0.13

Table S83. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	414	67.1 (13.1)
subtype1	172	69.8 (12.5)
subtype2	144	64.9 (12.7)
subtype3	98	65.3 (13.9)

Figure S75. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0885 (Chi-square test), Q value = 1

Table S84. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	66	1	31	120	9	1	22	13	50	35	42	15	1
subtype1	31	0	13	48	6	0	12	5	15	16	24	1	0
subtype2	18	0	9	42	1	1	6	5	22	11	12	11	0
subtype3	17	1	9	30	2	0	4	3	13	8	6	3	1

Figure S76. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.648 (Chi-square test), Q value = 1

Table S85. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	11	70	284	49
subtype1	5	34	116	17
subtype2	2	21	102	19
subtype3	4	15	66	13

Figure S77. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.35 (Chi-square test), Q value = 1

Table S86. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	242	99	73
subtype1	102	35	35
subtype2	78	41	24
subtype3	62	23	14

Figure S78. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 2.44e-07 (Chi-square test), Q value = 3.1e-05

Table S87. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	310	49	7	1	41
subtype1	144	24	1	0	0
subtype2	95	17	6	0	23
subtype3	71	8	0	1	18

Figure S79. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.352 (Fisher's exact test), Q value = 1

Table S88. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	198	217
subtype1	89	83
subtype2	63	81
subtype3	46	53

Figure S80. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0248 (Fisher's exact test), Q value = 1

Table S89. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	359	54
subtype1	146	24
subtype2	133	11
subtype3	80	19

Figure S81. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0545 (Fisher's exact test), Q value = 1

Table S90. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	412
subtype1	0	172
subtype2	3	141
subtype3	0	99

Figure S82. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 1.1e-07 (Chi-square test), Q value = 1.4e-05

Table S91. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	293	3	23	24
subtype1	146	1	20	0
subtype2	88	0	2	11
subtype3	59	2	1	13

Figure S83. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'RNAseq cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.426 (ANOVA), Q value = 1

Table S92. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	391	2.1 (4.5)
subtype1	170	2.2 (4.4)
subtype2	133	2.3 (5.4)
subtype3	88	1.5 (2.8)

Figure S84. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S93. Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	160	66	180

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0726 (logrank test), Q value = 1

Table S94. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	318	60	0.1 - 140.4 (12.7)
subtype1	155	29	0.1 - 140.4 (15.0)
subtype2	66	11	0.1 - 100.0 (9.7)
subtype3	97	20	0.9 - 83.8 (8.0)

Figure S85. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.000285 (ANOVA), Q value = 0.033

Table S95. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	405	67.3 (13.1)
subtype1	159	66.5 (12.5)
subtype2	66	62.6 (14.4)
subtype3	180	69.9 (12.5)

Figure S86. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.167 (Chi-square test), Q value = 1

Table S96. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	66	1	33	115	8	1	22	12	48	32	42	16	1
subtype1	24	1	9	53	2	1	8	5	23	11	11	7	1
subtype2	7	0	6	18	0	0	2	3	10	8	4	4	0
subtype3	35	0	18	44	6	0	12	4	15	13	27	5	0

Figure S87. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.358 (Chi-square test), Q value = 1

Table S97. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	11	69	279	46
subtype1	5	24	115	15
subtype2	0	9	46	11
subtype3	6	36	118	20

Figure S88. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.312 (Chi-square test), Q value = 1

Table S98. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	239	95	71
subtype1	98	39	22
subtype2	33	19	14
subtype3	108	37	35

Figure S89. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 1.02e-05 (Chi-square test), Q value = 0.0012

Table S99. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	305	50	7	1	36
subtype1	113	12	5	1	26
subtype2	48	8	0	0	9
subtype3	144	30	2	0	1

Figure S90. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.441 (Fisher's exact test), Q value = 1

Table S100. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	191	215
subtype1	74	86
subtype2	27	39
subtype3	90	90

Figure S91. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.095 (Fisher's exact test), Q value = 1

Table S101. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	351	53
subtype1	145	15
subtype2	53	13
subtype3	153	25

Figure S92. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'MIRSEQ CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.07 (Fisher's exact test), Q value = 1

Table S102. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	403
subtype1	0	160
subtype2	2	64
subtype3	1	179

Figure S93. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 7.93e-06 (Chi-square test), Q value = 0.00097

Table S103. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	280	3	25	22
subtype1	92	1	2	15
subtype2	43	0	0	5
subtype3	145	2	23	2

Figure S94. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0625 (ANOVA), Q value = 1

Table S104. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	382	2.0 (4.5)
subtype1	146	1.6 (3.0)
subtype2	61	3.2 (8.0)
subtype3	175	2.0 (3.8)

Figure S95. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S105. Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	81	182	143

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.122 (logrank test), Q value = 1

Table S106. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	318	60	0.1 - 140.4 (12.7)
subtype1	77	15	0.2 - 135.7 (14.0)
subtype2	101	21	0.1 - 83.8 (8.1)
subtype3	140	24	0.1 - 140.4 (12.8)

Figure S96. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.0021 (ANOVA), Q value = 0.24

Table S107. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	405	67.3 (13.1)
subtype1	80	67.2 (13.0)
subtype2	182	69.6 (12.5)
subtype3	143	64.5 (13.3)

Figure S97. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0107 (Chi-square test), Q value = 1

Table S108. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	66	1	33	115	8	1	22	12	48	32	42	16	1
subtype1	13	0	5	32	3	1	3	1	7	7	3	3	1
subtype2	35	0	17	44	4	0	14	4	16	14	28	5	0
subtype3	18	1	11	39	1	0	5	7	25	11	11	8	0

Figure S98. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 0.659 (Chi-square test), Q value = 1

Table S109. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	11	69	279	46
subtype1	3	14	55	9
subtype2	6	36	121	19
subtype3	2	19	103	18

Figure S99. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.0252 (Chi-square test), Q value = 1

Table S110. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	239	95	71
subtype1	57	10	13
subtype2	105	41	36
subtype3	77	44	22

Figure S100. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 2.16e-06 (Chi-square test), Q value = 0.00027

Table S111. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	305	50	7	1	36
subtype1	63	4	2	1	10
subtype2	145	31	2	0	1
subtype3	97	15	3	0	25

Figure S101. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.312 (Fisher's exact test), Q value = 1

Table S112. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	191	215
subtype1	34	47
subtype2	93	89
subtype3	64	79

Figure S102. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 1 (Fisher's exact test), Q value = 1

Table S113. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	351	53
subtype1	71	10
subtype2	156	24
subtype3	124	19

Figure S103. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.597 (Fisher's exact test), Q value = 1

Table S114. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	403
subtype1	0	81
subtype2	1	181
subtype3	2	141

Figure S104. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS.OF.RESECTION'

P value = 7.85e-06 (Chi-square test), Q value = 0.00097

Table S115. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	280	3	25	22
subtype1	42	0	2	6
subtype2	147	2	23	2
subtype3	91	1	0	14

Figure S105. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.524 (ANOVA), Q value = 1

Table S116. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	382	2.0 (4.5)
subtype1	74	1.5 (3.0)
subtype2	177	2.2 (4.4)
subtype3	131	2.2 (5.4)

Figure S106. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S117. Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	79	78	63

'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.395 (logrank test), Q value = 1

Table S118. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	214	40	0.1 - 140.4 (13.1)
subtype1	75	15	0.1 - 140.4 (15.0)
subtype2	76	15	0.1 - 135.7 (13.1)
subtype3	63	10	0.2 - 62.8 (12.0)

Figure S107. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.12 (ANOVA), Q value = 1

Table S119. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	219	65.3 (13.2)
subtype1	79	66.7 (11.2)
subtype2	77	66.3 (14.0)
subtype3	63	62.4 (14.3)

Figure S108. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.466 (Chi-square test), Q value = 1

Table S120. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	30	1	15	69	3	1	8	8	32	18	15	11	1
subtype1	10	0	4	25	0	1	5	4	9	8	6	3	0
subtype2	15	1	7	26	2	0	2	3	10	3	3	3	1
subtype3	5	0	4	18	1	0	1	1	13	7	6	5	0

Figure S109. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.205 (Chi-square test), Q value = 1

Table S121. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	5	31	157	26
subtype1	2	12	58	7
subtype2	2	15	53	7
subtype3	1	4	46	12

Figure S110. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0279 (Chi-square test), Q value = 1

Table S122. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	129	55	35
subtype1	45	20	14
subtype2	55	16	6
subtype3	29	19	15

Figure S111. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.459 (Chi-square test), Q value = 1

Table S123. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	154	20	5	1	36
subtype1	59	7	2	0	10
subtype2	55	4	1	1	16
subtype3	40	9	2	0	10

Figure S112. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.312 (Fisher's exact test), Q value = 1

Table S124. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	96	124
subtype1	30	49
subtype2	39	39
subtype3	27	36

Figure S113. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 6.9e-05 (Fisher's exact test), Q value = 0.0081

Table S125. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	191	29
subtype1	78	1
subtype2	62	16
subtype3	51	12

Figure S114. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'MIRseq Mature CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.744 (Fisher's exact test), Q value = 1

Table S126. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	2	218
subtype1	1	78
subtype2	0	78
subtype3	1	62

Figure S115. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.45 (Chi-square test), Q value = 1

Table S127. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	128	1	2	20
subtype1	43	0	1	7
subtype2	43	1	1	10
subtype3	42	0	0	3

Figure S116. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.042 (ANOVA), Q value = 1

Table S128. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	200	2.0 (4.7)
subtype1	72	1.8 (3.1)
subtype2	69	1.1 (2.6)
subtype3	59	3.2 (7.3)

Figure S117. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S129. Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	21	86	113

'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.162 (logrank test), Q value = 1

Table S130. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	214	40	0.1 - 140.4 (13.1)
subtype1	20	3	0.1 - 139.2 (14.6)
subtype2	86	16	0.1 - 100.0 (10.4)
subtype3	108	21	0.1 - 140.4 (14.3)

Figure S118. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.155 (ANOVA), Q value = 1

Table S131. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	219	65.3 (13.2)
subtype1	21	65.7 (13.1)
subtype2	86	63.2 (14.0)
subtype3	112	66.9 (12.5)

Figure S119. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.947 (Chi-square test), Q value = 1

Table S132. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	30	1	15	69	3	1	8	8	32	18	15	11	1
subtype1	3	0	2	5	0	0	1	0	5	2	3	0	0
subtype2	10	0	7	25	1	0	2	3	14	9	6	6	0
subtype3	17	1	6	39	2	1	5	5	13	7	6	5	1

Figure S120. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.723 (Chi-square test), Q value = 1

Table S133. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T0+T1	T2	T3	T4
ALL	5	31	157	26
subtype1	0	3	15	3
subtype2	1	11	61	13
subtype3	4	17	81	10

Figure S121. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.419 (Chi-square test), Q value = 1

Table S134. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	129	55	35
subtype1	11	6	4
subtype2	45	26	15
subtype3	73	23	16

Figure S122. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.86 (Chi-square test), Q value = 1

Table S135. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	154	20	5	1	36
subtype1	14	3	0	0	4
subtype2	60	10	2	0	13
subtype3	80	7	3	1	19

Figure S123. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.865 (Fisher's exact test), Q value = 1

Table S136. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	96	124
subtype1	10	11
subtype2	36	50
subtype3	50	63

Figure S124. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0703 (Fisher's exact test), Q value = 1

Table S137. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA
ALL	191	29
subtype1	19	2
subtype2	69	17
subtype3	103	10

Figure S125. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.334 (Fisher's exact test), Q value = 1

Table S138. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	2	218
subtype1	0	21
subtype2	2	84
subtype3	0	113

Figure S126. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.445 (Chi-square test), Q value = 1

Table S139. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	128	1	2	20
subtype1	12	0	0	4
subtype2	58	0	0	7
subtype3	58	1	2	9

Figure S127. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.069 (ANOVA), Q value = 1

Table S140. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	200	2.0 (4.7)
subtype1	17	4.3 (12.1)
subtype2	77	2.1 (3.7)
subtype3	106	1.5 (2.9)

Figure S128. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Methods & Data

Input

Cluster data file = COAD-TP.mergedcluster.txt
Clinical data file = COAD-TP.merged_data.txt
Number of patients = 433
Number of clustering approaches = 12
Number of selected clinical features = 11
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References

[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)

[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)

[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

[5] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[6] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)

[7] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)

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